Optimization of assembly line balancing with energy efficiency by using tiki-taka algorithm
Assembly line balancing (ALB) could be translated as the activity that is applied to optimize the layout of an assembly line by distributing a balance workload assembly among workstations. Based on the previous research conducted by researchers, most of the assembly line model studies focused extens...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2023
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38492/1/ir.Optimization%20of%20assembly%20line%20balancing%20with%20energy%20efficiency%20by%20using%20tiki-taka%20algorithm.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Assembly line balancing (ALB) could be translated as the activity that is applied to optimize the layout of an assembly line by distributing a balance workload assembly among workstations. Based on the previous research conducted by researchers, most of the assembly line model studies focused extensively on the problem models that related to time, space, workers, and a few resources. However, there is a shortage of studies that considers the utilization of electrical energy in assembly line design. This situation stimulates this research to further investigate the Assembly Line Balancing with Energy Efficiency (ALB-EE). This research aimed to establish a computational model that represents the ALB-EE, propose a new Tiki-Taka Algorithm (TTA) to solve and optimize the ALB-EE and validate the developed model through a real-life case study. In the modeling phase, all the ALB-EE optimization objectives are presented in a mathematical form to earn line efficiency and energy utilization. Then, the TTA is developed before undergoing functionality tests by benchmarking with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA). Lastly, a study of the industrial case was performed as a validation of the developed model and algorithm. An automotive company is selected, and the collected actual data is used for validation purposes. As a result, the Optimized TTA performs best compared to PSO, GWO, GA, and WOA in most of the test problems. Meanwhile, the case study validation activity resulting an increase in line efficiency from 92.7% to 95.1% by task arrangement with the utilization of TTA. Through the improved line efficiency, the total energy consumed is also reduced to 3,305,478.46 J from the initial figure of 3,374,329.46 J. This is a clear indication that the developed TTA algorithm is reliable and could be used in optimizing a real-life problem by the resequence of the assembly task, thus reducing the cycle time and could reduce the total energy consumption by machinery |
|---|